Creates a wrapper, which can be used like any other learner object.<p></p><p>Fitting is performed in a weighted fashion where each observation receives a weight,
depending on the class is belongs to, see <code>wcw.weight</code>.
This might help to mitigate problems in imbalanced classification problems.</p>

makeWeightedClassesWrapper

Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There is
    also a n experimental extension for surival analysis and cost-sensitive
    learning. Generic resampling, including cross-validation, bootstrapping and
    subsampling. Hyperparameter tuning with modern optimization techniques.
    Filter and wrapper methods for feature selection. Extension of basic
    learners with additional operations common in machine learning.

makeWeightedClassesWrapper: Wraps a classifier for weighted fitting where each class receives a weight.

Description

Usage

Arguments

Value